Unpaired image-to-image (I2I) translation plays an important role in denoising, super-resolution and modality conversion. These methods often rely on adversarial training for matching two distributions, and they suffer from hallucinations induced by biases. In this work, we study a spatial shift bias in the case of domain translation for retinal optical coherence tomography (OCT). For one domain, the retinas are at the bottom of the image, while in the other domain, the retinas are centered. We show that the conventional PatchGAN discriminator replicates the spatial bias of the target domain. This leads to imperfectly translated images. By putting explicit limitations to the receptive field of the PatchGAN, we recover the ability of the I2I network to truthfully translate OCTs from one domain to the other. Evidence is provided by improvements of Fréchet Inception distance, increased translational equivariance, and increased Dice scores.